CN114611290B - Method for forecasting field flood hydrological model in real time based on quantitative change parameter hydrological uncertainty processor - Google Patents
Method for forecasting field flood hydrological model in real time based on quantitative change parameter hydrological uncertainty processor Download PDFInfo
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Abstract
The invention discloses a field flood hydrological model real-time forecasting method based on a quantitative variation parameter hydrological uncertainty processor, which comprises the steps of collecting and arranging data of a research basin; constructing a hydrological model; constructing a secondary flood forecasting model; respectively designing objective functions for each field flood in the group; with the minimum objective function as a target, simultaneously optimizing the objective functions of the multiple floods in the group by using an objective function optimization algorithm to determine optimal parameters; determining marginal distribution and empirical distribution of an actually measured flow process and a forecast flow process; calculating a prior density function, a likelihood function and posterior distribution of the actually measured flow on a conversion space; a posterior distribution function of actual measurement flow of flood in the original space field is calculated; a hydrological uncertainty processor based on quantitative parameters predicts a secondary flood runoff process. The advantages are that: under the conditions that the difference of flood product convergence characteristics of the whole field of the drainage basin is large and the parameter adjusting person does not have rich parameter adjusting experience, the real-time flood forecast result with high precision can be obtained.
Description
Technical Field
The invention relates to the technical field of hydrological forecasting, in particular to a field flood hydrological model real-time forecasting method based on a quantitative change parameter hydrological uncertainty processor.
Background
At present, in order to improve the accuracy of real-time flood forecasting, it is one of the indispensable steps to analyze the uncertainty of hydrologic forecasting and continuously correct the forecasting result of the hydrologic model according to the error of hydrologic forecasting. The currently widely adopted method for correcting the prediction result of the hydrological model is to adopt an autoregressive correction model or a Bayesian correction model, wherein the autoregressive correction model carries out real-time prediction on the hydrological variable by establishing the self-correlation relationship of the hydrological variable and continuously correcting the model parameters through real-time observed data. The method is simple and convenient to calculate and strong in operability, however, when the correlation between the time periods before and after the hydrological variable is not strong, the simulation precision of the method is low. The Bayesian correction model establishes an actual measurement data distribution function based on known actual measurement data and hydrological model forecast data through a Bayesian formula, so that a probability estimation interval of the actual measurement data is obtained. The method makes full use of the forecast data of the hydrological model and the known measured data, and has good application effect.
However, the model has strong dependency on the prior distribution and likelihood function of the measured flow and the forecast flow when in application, the parameters of the secondary flood forecast model are different for floods with different flood peak magnitudes, the difference between the distribution functions of the measured flow and the forecast flow is large, and the accuracy of the Bayesian correction model is often low. Therefore, a forecasting method based on a quantitative variation parameter hydrological uncertainty processor is needed to realize field flood real-time forecasting.
Disclosure of Invention
The invention aims to provide a field flood hydrological model real-time forecasting method based on a quantitative change parameter hydrological uncertainty processor, so as to solve the problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for forecasting a field flood hydrological model in real time based on a quantitative variable parameter hydrological uncertainty processor comprises the following steps,
s1, collecting and arranging the data of a research basin;
s2, constructing a hydrological model suitable for researching the daily scale of basin runoff forecasting;
s3, constructing a secondary flood forecasting model suitable for researching secondary flood forecasting of the watershed field, and providing an initial state variable for the secondary flood forecasting model based on the hydrological model of the daily scale;
s4, constructing a target function of each field of flood in the group based on a secondary flood forecasting model;
s5, with the minimum objective function as a target, simultaneously optimizing objective functions of multiple fields of secondary floods in the group by using an objective function optimization algorithm, and determining optimal parameters of a comprehensive field flood hydrological model in the group based on a secondary flood forecasting model;
s6, determining marginal distribution and empirical distribution of an actual measurement flow process and a forecast flow process based on the optimal parameters;
s7, calculating a prior density function, a likelihood function and posterior distribution of the actually measured flow on the conversion space;
s8, a posterior distribution function of actual flood measured flow of the original space field is calculated;
and S9, forecasting the secondary flood runoff process by the hydrological uncertainty processor based on the quantitative variation parameters.
Preferably, step S1 specifically includes the following steps,
s11, collecting a daily rainfall amount series and a rainfall amount extract table of each rainfall station in a research basin, a daily evaporation amount series of hydrological stations in the research basin, a daily average flow amount series of an outlet section of the research basin and a flood element extract table;
s12, interpolating the runoff process of the field flood into a time-interval-by-time flow series with the time interval length of 1 hour by a linear interpolation method; interpolating the rainfall amount series in the field flood process into a time-interval-by-time rainfall amount series with the time interval length of 1 hour by a linear interpolation method; and calculating the surface average rainfall amount series and the surface average evaporation amount series of the research watershed by a Thiessen polygon method or an arithmetic mean method.
Preferably, the step S2 is specifically to construct a hydrological model suitable for daily scale of basin runoff research forecasting, and calculate state variables of each day of the basin research by using the hydrological model to provide initial state variables for a secondary flood forecasting model of the basin research; the state variables comprise soil water content, runoff yield area ratio and free water storage capacity.
Preferably, step S3 specifically includes the following steps,
s31, constructing a secondary flood forecasting model suitable for researching the sink flow characteristics of the drainage basin, and setting constraint conditions;
s32, grouping and integrating the flood of the research basin by taking the flood peak magnitude as a grouping condition, selecting representative groups of flood and determining the starting and stopping time of each flood;
and S33, calculating initial state variables of flood of each field by using the hydrological model of daily scale constructed in the S2.
Preferably, the step S4 specifically includes selecting a target function for each flood according to the optimization effect on the single flood; the target function is a residual sum of squares function or a peak weighted root mean square error function or a flood error percentage function or a comprehensive function comprehensively considering the influence of flood peak and flood.
Preferably, step S5 specifically includes the following steps,
s51, selecting an optimization algorithm based on super-multi-objective large-scale optimization, simultaneously optimizing the objective functions of the floods in each field in the group by taking the minimum objective function as an optimization objective, and determining a pareto solution of optimal parameters of a comprehensive field flood hydrological model in the group;
s52, carrying out pareto solution on optimal parameters of the field-level flood hydrological model in the comprehensive analysis group, and selecting the parameter which enables the rate periodic forecast qualification rate to be highest as the optimal parameter of the field-level flood hydrological model in the group;
based on the initial state variables of each field of secondary flood, the secondary flood forecasting model adopts a Ma Sijing root method or a dead time algorithm to carry out river flood calculation so as to determine the periodic forecasting qualification rate of the rate; wherein the Ma Sijing root method has the constraint condition that,
wherein k is the flood propagation time when the river is a constant flow; x is a flow specific gravity coefficient; Δ t is the period length.
Preferably, step S6 specifically includes the following steps,
s61, substituting the optimal parameters of the flood hydrological model of the comprehensive field in the group obtained in the step S5 into a secondary flood forecasting model, and calculating the period-by-period runoff process of each field of flood in the group to obtain a forecasting flow series S of each field of flood in the group;
s62, respectively calculating the empirical distribution of the actual measurement flow series H of the flood peak magnitude field flood and the corresponding forecast flow series S according to a mathematical expectation formula;
s63, selecting a marginal distribution series commonly used by hydrological variables, selecting marginal distributions best fitted with empirical distributions of the field flood actual measured flow series and the corresponding forecast flow series by utilizing a BIC (building information center) criterion as theoretical marginal distribution functions of the field flood actual measured flow series and the corresponding forecast flow series of the flood peak magnitude respectively, and recording the functions as gamma i And Λ i (ii) a The calculation formula of the BIC criterion is that,
BIC＝ln(n)k-2ln(L)
wherein, BIC is a BIC value of a certain marginal distribution; n is the number of samples, namely the total time period number of the field flood of the flood peak magnitude; k is the number of marginal distribution function parameters; l is a likelihood function.
Preferably, the step S7 is specifically to convert the measured flood flow series H and the corresponding forecast flow series S of the same peak magnitude into the measured flood flow series W and the corresponding forecast flow series X in space by a normal quantile conversion method, and further to calculate a prior density function, a likelihood function, and a posterior distribution of the measured flood flow in the conversion space; the formula for the conversion of the normal quantile is,
W i ＝Q -1 (Γ i (H i )), i＝1,2,…,12
X i ＝Q -1 (Γ i (S i )), i＝1,2,…,12
wherein, Q is a standard normal distribution function, and i is the ith time interval.
Preferably, step S8 is specifically to convert the posterior distribution of the actual measurement flow series of the flood of the same session to be forecasted in the conversion space into a posterior distribution function in the original space by using the jacobian formula; the formula of the Jacobian is as follows,
J(y)＝m(y)/q(Q -1 (M(y)))
wherein m is a density function of a field flood actual measurement flow series H; y is field flood actual measurement flow series H of the same flood peak magnitude to be forecasted 0 (ii) a q is a standard normal distribution density function; q is a standard normal distribution function; m is the edge distribution function gamma of the field flood actual measurement flow series H.
Preferably, step S9 is specifically to measure the actual measured flow rate series H with the period length of 1 hour for field flood with the same peak magnitude to be forecasted 0 Flow forecasting series S of secondary flood forecasting model 1 And (4) substituting the measured flow into the posterior distribution of the original space field flood measured flow obtained in the step (S8), randomly sampling the distribution function by a random sampling method, calculating the quantiles of 50%, 2.5% and 97.5% of the measured flow, taking the quantiles of 50% as the real-time flow forecasting result of the hydrological uncertainty processor, and taking the quantiles of 2.5% and 97.5% as the upper limit and the lower limit of a 95% confidence interval of the real-time flow forecasting.
The invention has the beneficial effects that: 1. and simultaneously optimizing the objective functions of all flood fields of the same magnitude by adopting an optimization algorithm based on ultra-multi-objective large-scale optimization, and calculating the forecast flow of each flood field by utilizing the optimized hydrological model, so that a hydrological uncertainty processor based on the flood peak magnitude is established, and the flood fields to be forecasted are forecast in real time by the established hydrological uncertainty processor. 2. Under the conditions that the difference of flood product convergence characteristics of the whole field of the drainage basin is large and the parameter adjusting person does not have rich parameter adjusting experience, the real-time flood forecasting result with high precision can still be obtained through simple operation.
Drawings
FIG. 1 is a schematic flow chart of a real-time forecasting method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the position of a Thiessen polygon and a hydrological station in a watershed above a mountain according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the calculation results of a Japanese scale hydrological model in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a pareto front in an embodiment of the present invention;
FIG. 5 is a diagram illustrating values of hydrological model parameters corresponding to pareto fronts in an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a measured flow margin distribution curve of a secondary flood in an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a secondary flood forecast flow margin distribution curve according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating the forecasting results of the secondary flood forecasting model in an embodiment of the present invention;
FIG. 9 is a diagram illustrating the forecast results of the hydrological uncertainty processor in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example one
As shown in fig. 1, in the present embodiment, a method for forecasting a flood hydrological model in real time in a field based on a quantitative parameter hydrological uncertainty processor is provided, which includes the following steps,
s1, collecting and arranging the data of a research basin;
s2, constructing a hydrological model suitable for researching the daily scale of basin runoff forecasting;
s3, constructing a secondary flood forecasting model suitable for researching secondary flood forecasting of the watershed field, and providing an initial state variable for the secondary flood forecasting model based on the hydrological model of the daily scale;
s4, constructing a target function of each field of flood in the group based on a secondary flood forecasting model;
s5, with the minimum objective function as a target, optimizing the objective function of the multiple-field secondary flood in the group by using an objective function optimization algorithm, and determining the optimal parameters of a comprehensive field-time flood hydrological model in the group based on a secondary flood forecasting model;
s6, determining marginal distribution and empirical distribution of an actual measurement flow process and a forecast flow process based on the optimal parameters;
s7, calculating a prior density function, a likelihood function and posterior distribution of the actually measured flow on the conversion space;
s8, a posterior distribution function of actual flood measured flow of the original space field is calculated;
and S9, forecasting the secondary flood runoff process by the hydrological uncertainty processor based on the quantitative variation parameters.
In this embodiment, the real-time forecasting method specifically includes nine parts, which are respectively data collection and compilation, hydrological model construction, sub-flood forecasting model construction, target function setting, simultaneous optimization of the target functions of multiple sub-floods in a group, marginal distribution and empirical distribution of the actual measurement flow process and the forecasting flow process determination, prior density function and likelihood function and posterior distribution of the actual measurement flow in a conversion space, posterior distribution function of the actual measurement flow in an original space, and sub-flood runoff forecasting process based on a quantitative variable parameter hydrological uncertainty processor. The following explains these nine parts in detail.
1. Data collection and compilation
This section corresponds to step S1, and specifically includes the following,
s11, collecting a daily rainfall amount series and a rainfall amount extraction table of each rainfall station in a research flow field, a daily evaporation amount series of hydrological stations in the research flow field, a daily average flow amount series of an outlet section of the research flow field and a flood element extraction table;
s12, interpolating the runoff process of the field flood into a time-interval-by-time flow series with the time interval length of 1 hour by a linear interpolation method; interpolating the rainfall amount series in the field flood process into a time-interval-by-time rainfall amount series with the time interval length of 1 hour by a linear interpolation method; calculating a surface average rainfall series of the research basin by a surface average rainfall calculation method such as a Thiessen polygon method or an arithmetic mean method; and calculating and researching the average evaporation capacity series of the watershed surface by methods such as a Thiessen polygon method, an arithmetic mean method and the like.
2. Construction of hydrological model
The method comprises the following steps of S2, specifically, constructing a hydrological model suitable for researching daily scale of basin runoff forecast, and calculating state variables of the basin in each day, including soil water content, runoff yield area ratio, free water demand and the like, by using the hydrological model.
The constructed daily scale hydrological model can provide initial state variables for a secondary flood forecasting model for researching the watershed.
3. Constructing secondary flood forecasting model
This section corresponds to step S3, and includes in particular the following,
s31, constructing a secondary flood forecasting model suitable for researching the sink flow characteristics of the drainage basin, and setting constraint conditions;
s32, grouping and integrating the flood of the research basin by taking the flood peak magnitude as a grouping condition, selecting representative groups of flood and determining the starting and stopping time of each flood;
and S33, calculating initial state variables of flood of each field by using the hydrological model of daily scale constructed in the S2.
4. Setting an objective function
The part corresponds to the step S4, and specifically, a target function is respectively selected for each flood according to the optimization effect of the single flood; the target function is a residual sum of squares function or a peak weighted root mean square error function or a flood error percentage function or a comprehensive function comprehensively considering the influence of flood peak and flood.
The objective function can be selected according to the optimization effect of single-field flood, several objective functions are designed for several floods in the group, and the objective functions of the floods in each field can be different or the same according to actual needs.
5. Simultaneous optimization of objective functions for multi-level floods within a group
This section corresponds to step S5, and includes in particular the following,
s51, selecting an optimization algorithm based on the super-multi-objective large-scale optimization, taking the minimum of each objective function as an optimization objective, simultaneously optimizing the objective function of each field of flood in the group, and determining pareto solutions of optimal parameters of the field of flood hydrological models in the group;
s52, carrying out pareto solution on the optimal parameters of the field sub-flood hydrological model in the comprehensive analysis group, and selecting the parameter which enables the rate periodic forecast qualified rate to be highest as the optimal parameter of the field sub-flood hydrological model in the group;
each flood has an objective function, n objective functions are provided for n floods in the group, an optimization algorithm based on super-multi-objective large-scale optimization, such as a high-efficiency large-scale multi-objective optimization algorithm based on a competitive group optimizer, is selected, the objective function of each flood in the group is optimized with the minimum objective function as the optimization target, a pareto solution of the optimal parameters of the intra-group field sub-flood hydrological model is determined, and the parameters with the highest rate-fixed forecast qualification rate are selected as the optimal parameters of the intra-group comprehensive field sub-flood hydrological model through comprehensive analysis.
The optimization algorithm based on the ultra-multi-objective large-scale optimization can simultaneously optimize the objective function of flood of more than 3 fields, and meanwhile, the optimization can also be performed on the condition that the number of parameters of the flood hydrological model of a field is more than 100.
In step S52, based on the initial state variables of each flood, the secondary flood forecasting model adopts a Ma Sijing root method or a time lag algorithm to carry out river flood calculation so as to determine the regular forecasting qualification rate of the rate; wherein the Ma Sijing root method has the constraint condition that,
wherein k is the flood propagation time when the river is a constant flow; x is a flow specific gravity coefficient; Δ t is the period length.
6. Determining marginal and empirical distributions for measured and predicted flow processes
This section corresponds to step S6, and includes in particular the following,
s61, substituting the optimal parameters of the flood hydrological model of the comprehensive field in the group obtained in the step S5 into a secondary flood forecasting model, and calculating the period-by-period runoff process of each field of flood in the group to obtain a forecasting flow series S of each field of flood in the group;
s62, respectively calculating the empirical distribution of the actual measurement flow series H of the flood peak magnitude field flood and the corresponding forecast flow series S according to a mathematical expectation formula;
s63, selecting a marginal distribution series commonly used by hydrological variables, selecting marginal distributions best fitted with empirical distributions of the field flood actual measured flow series and the corresponding forecast flow series by utilizing a BIC (building information center) criterion as theoretical marginal distribution functions of the field flood actual measured flow series and the corresponding forecast flow series of the flood peak magnitude respectively, and recording the functions as gamma i And Λ i (ii) a The calculation formula of the BIC criterion is that,
BIC＝ln(n)k-2ln(L)
wherein, BIC is a BIC value of a certain marginal distribution; n is the number of samples, namely the total time period number of the field flood of the flood peak magnitude; k is the number of marginal distribution function parameters; l is a likelihood function.
The selected marginal distributions include, but are not limited to, the following: beta distribution, exponential distribution, extremum distribution, gamma distribution, generalized extremum distribution, inverse gaussian distribution, logistic distribution, log-normal distribution, nakagami distribution, normal distribution, rayleigh distribution, rician distribution, generalized pareto distribution, t distribution considering position and scale, weibull distribution, log weibull distribution, and the like.
7. Calculating prior density function and likelihood function and posterior distribution of measured flow in conversion space
The part corresponds to the step S7, specifically, a field flood actual measurement flow series H and a corresponding forecast flow series S with the same flood peak magnitude are converted into a field flood actual measurement flow series W and a corresponding forecast flow series X in space through a normal quantile conversion method, and then a prior density function, a likelihood function and posterior distribution of actual measurement flow in the conversion space are calculated; the formula for the conversion of the normal quantile is,
W i ＝Q -1 (Γ i (H i )), i＝1,2,…,12
X i ＝Q -1 (Γ i (S i )), i＝1,2,…,12
wherein, Q is a standard normal distribution function, and i is the ith time interval.
8. Calculating posterior distribution function of original space actual measurement flow
The part corresponds to the step S8, and concretely, the posterior distribution of the actual measurement flow series of the flood of the same field to be forecasted on a conversion space is converted into a posterior distribution function on an original space through a Jacobian formula; the formula of the Jacobian is as follows,
J(y)＝m(y)/q(Q -1 (M(y)))
wherein m is a density function of a field flood actual measurement flow series H; y is field flood actual measurement flow series H of the same flood peak magnitude to be forecasted 0 (ii) a q is a standard normal distribution density function; q is a standard normal distribution function; m is the edge distribution function gamma of the field flood actual measurement flow series H.
9. Hydrographic uncertainty processor forecasting secondary flood runoff process based on quantitative change parameters
The part corresponds to step S9, specifically, the actual measurement flow rate series H with the period length of 1 hour for field flood with the same peak magnitude to be forecasted 0 Flow forecasting series S of secondary flood forecasting model 1 And substituting the measured flow into the posterior distribution of the original space field flood measured flow obtained in the step S8, randomly sampling the distribution function by a random sampling method, calculating quantiles of 50%, 2.5% and 97.5% of the measured flow, taking the quantile of 50% as a real-time flow prediction result of the hydrological uncertainty processor, and taking the quantiles of 2.5% and 97.5% as an upper limit and a lower limit of a 95% confidence interval of real-time flow prediction.
Example two
In the embodiment, real-time flood forecasting of the watershed field of the steep ridge at the upstream of the Huaihe river is taken as an example to show the effect achieved by the method.
The large slope ridge station is the most upstream hydrological station of the Huaihe main stream and controls the area of a basin by 1640 square kilometers. The river flow above the hillside is 73 kilometers long, and most of the river basin is mountainous and hilly, so that the vegetation is good. The river belongs to a mountain stream river, has more branches, large gradient, fast confluence and rapid water flow, and is easy to cut off during drought. The water conservancy projects in the river basin are few, and crops mainly comprise rice. There are 4 hydrological stations in the watershed above the hillside. In the embodiment, the starting and stopping time is the daily rainfall data of 4 hydrological stations from 1/1999 to 31/2009/12/2009, the daily evaporation capacity of the sabina chinensis station, the daily flow data of the hillside station, and the four-field flood peak flow rate is more than 1500m 3 On the basis of flood runoff data of/s and corresponding rainfall extract data, evaporation capacity data and the like, the peak flow of the watershed above the large hillside is more than 1500m 3 And (4) forecasting the runoff process of flood in the field/s in real time. The method for forecasting the flood hydrological model in real time in the field based on the quantitative variable parameter hydrological uncertain processor comprises the following steps:
1. data collection and compilation
Collecting the daily rainfall data of 4 hydrological stations in the basin from 1/2009 to 31/2009 in 1999, the daily evaporation capacity data of the Turber station and the daily average flow data of the mountain ridge station. Selecting peak flow rate greater than 1500m 3 4 times of flood runoff data of/s and corresponding rainfall extract data, and interpolating the flood runoff process of the field into a runoff process with the time interval length of 1 hour by a linear interpolation methodInterpolating rainfall extract data of each rainfall station in the flow domain into a rainfall process with the time course length of 1 hour; collecting DEM data of a watershed above a large-slope hydrological station and longitude and latitude data of 4 hydrological stations, extracting a watershed water system diagram by utilizing GIS software, obtaining watershed area data, dividing Thiessen polygons to determine the area weight of each hydrological station, and calculating an average surface rainfall series of the watershed. The Thiessen polygon map of the watershed above the hillside and the position of each hydrological station are shown in the attached figure 2. By calculation, the weight of the Thiessen polygon for the four rain stations is shown in Table 1 below:
TABLE 1 Thessen polygon weight for each hydrological station
| Cortex phellodendri chinensis | Wu Cheng | Huanggang | Mountain slope |
| 0.26 | 0.29 | 0.32 | 0.13 |
2. Construction of hydrological model
A hydrological model of a daily scale suitable for forecasting runoff of a watershed of a large slope is constructed, a runoff producing module adopts a full runoff producing mode, an evaporation module adopts a three-layer evaporation mode, linear reservoir methods are adopted for slope confluence, interflow confluence and subsurface runoff confluence calculation, and a lag time algorithm is adopted for unit outflow. The calculation result of the constructed Japanese scale hydrological model is shown in the attached figure 3. And (4) calculating initial state variables of the 4-field flood in the first step by using the model, wherein the initial state variables comprise the soil water content, the runoff yield area ratio and the initial free water storage capacity of the upper and lower deep layers. For example, initial state variables of No. 20020623 flood are respectively upper layer soil water content WU =3.49mm; the water storage capacity of the lower soil is WL =5.54mm; the water storage capacity of the deep soil is WD =0mm; the area ratio FR =0.17; initial free water hold up S =0.18mm.
3. Constructing secondary flood forecasting model
Constructing a secondary flood forecasting model suitable for a large slope watershed, wherein the calculation time interval length is 1 hour, the runoff producing module adopts a full runoff producing mode, the evaporation module adopts a three-layer evaporation mode, the sloping field confluence adopts a unit line calculation method, the interflow confluence and subsurface runoff confluence calculation adopts a linear reservoir method, and the unit outflow adopts a time-lag algorithm. And adopting the calculation result in the second step for the secondary flood initial state variable.
4. Setting an objective function
And the four-field flood adopts a peak weighted root mean square error function as an objective function. The peak weighted root mean square error function is
Wherein f is a mean weighted root mean square error function; NQ is the number of vertical coordinates of the calculated process line; q. q.s 0 (i) The measured flow of the flood at the ith moment is measured; q. q.s s (i) The flow rate of flood at the ith moment for the output flood i calculated using the selected model parameters; q. q.s 0 (mean) is the average of the measured field flood flows.
5. Simultaneous optimization of objective functions for multi-level floods within a group
The objective function of the four-field flood is optimized simultaneously by adopting a high-efficiency large-scale multi-objective optimization algorithm based on a competitive cluster optimizer, and the optimized pareto frontier is shown in fig. 4. The hydrological model parameters of each group corresponding to the pareto frontier are shown in fig. 5. Selecting the 4 th group of parameters as final secondary flood model parameters, wherein the certainty coefficient of the forecast result of the No. 20020623 flood is 0.96; 5363 the certainty coefficient of the flood forecast result of 20050626 is 0.94; 5363 the certainty coefficient of the flood forecast result of 20050710 is 0.94; the certainty coefficient of the flood forecast result No. 20050829 is 0.96.
6. Determining marginal and empirical distributions for measured and predicted flow processes
According to BIC criterion, the measured flow of the flood No. 20020623, no. 20050626, no. 20050710 and No. 20050829 obeys generalized pareto distribution, and the distribution function is in the form of
The parameter is k =1.211; σ =34.1363; theta =3.63, and a secondary flood actual measurement flow marginal distribution curve is shown in an attached figure 6; the secondary flood forecast model of the four-field flood forecasts the flow rate to obey Weibull distribution, and the distribution function form is
The parameters are a =104.799, b =0.5198, and the secondary flood forecast flow marginal distribution curve is shown in fig. 7.
7. Calculating prior density function and likelihood function and posterior distribution of measured flow in conversion space
The prior density function of the measured runoff series of the field flood on the conversion space is
Wherein, the small scale Q is a prior density function in a conversion space; w is a 0 The value of the actual measurement data at the moment facing the forecast on a conversion space is calculated; w is a 1 The value of the actual measurement flow rate after 1 hour at the moment is on the conversion space; q is a standard normal distribution density function.
Converting the likelihood function in space to
Wherein x is 1 Is a value representing the forecast flow at 1 hour after the face time on the transition space.
8. Calculating posterior distribution function of original space actual measurement flow
The posterior distribution function of the actual measurement runoff process of No. 20030630 flood to be forecasted in the original space is as follows:
wherein h is 0 The value of actual measurement data of the moment facing the forecast on the original space is predicted; h is 1 The value of the actual measured flow rate after 1 hour facing the moment is on the original space. s 1 The value of the forecast flow on the original space after 1 hour ahead of time.
9. Hydrographic uncertainty processor forecasting secondary flood runoff process based on quantitative change parameters
The forecasting results of the 20030630 flood secondary flood forecasting model are shown in fig. 8, and the forecasting results of the hydrologic uncertainty processor based on the time-varying parameters are shown in fig. 9. The Nash coefficient of the flood forecast for the river basin 20020630 of the great slope ridge is 0.93 through calculation through the Xinanjiang model, and the Nash coefficient forecast through the hydrological uncertainty processor based on the time-varying parameters is 0.96, so that the accuracy improvement effect is remarkable.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a field flood hydrological model real-time forecasting method based on a quantitative variable parameter hydrological uncertainty processor, which adopts an optimization algorithm based on super-multi-objective large-scale optimization to simultaneously optimize objective functions of all field floods of the same magnitude, calculates the forecasting flow of each field flood by using the optimized hydrological model, thereby establishing the hydrological uncertainty processor based on the flood peak magnitude, and carries out real-time forecasting on the field flood to be forecasted by the established hydrological uncertainty processor. According to the method, under the conditions that the difference of flood product convergence characteristics of the whole field of the drainage basin is large and parameter adjusting persons do not have abundant parameter adjusting experience, the real-time flood forecast result with high precision can still be obtained through simple operation.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (8)
1. A field flood hydrological model real-time forecasting method based on a quantitative change parameter hydrological uncertainty processor is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, collecting and arranging the data of a research basin;
s2, constructing a hydrological model suitable for researching the daily scale of basin runoff forecasting;
s3, constructing a secondary flood forecasting model suitable for researching secondary flood forecasting of the watershed field, and providing an initial state variable for the secondary flood forecasting model based on the hydrological model of the daily scale;
s4, constructing a target function of each field of flood in the group based on a secondary flood forecasting model;
s5, with the minimum objective function as a target, simultaneously optimizing objective functions of multiple fields of secondary floods in the group by using an objective function optimization algorithm, and determining optimal parameters of a comprehensive field flood hydrological model in the group based on a secondary flood forecasting model; the step S5 specifically includes the following contents,
s51, selecting an optimization algorithm based on super-multi-objective large-scale optimization, simultaneously optimizing the objective functions of the floods in each field in the group by taking the minimum objective function as an optimization objective, and determining a pareto solution of optimal parameters of a comprehensive field flood hydrological model in the group;
s52, carrying out pareto solution on optimal parameters of the field-level flood hydrological model in the comprehensive analysis group, and selecting the parameter which enables the rate periodic forecast qualification rate to be highest as the optimal parameter of the field-level flood hydrological model in the group;
based on the initial state variables of each field of secondary flood, the secondary flood forecasting model adopts a Ma Sijing root method or a dead time algorithm to carry out river flood calculation so as to determine the periodic forecasting qualification rate of the rate; the constraint conditions of the Ma Sijing root method are as follows:
wherein k is flood propagation time when the river is constant flow; x is a flow specific gravity coefficient; Δ t is the period length;
s6, determining marginal distribution and empirical distribution of an actual measurement flow process and a forecast flow process based on the optimal parameters;
the step S6 specifically includes the following contents,
s61, substituting the optimal parameters of the flood hydrological model of the comprehensive field in the group obtained in the step S5 into a secondary flood forecasting model, and calculating the period-by-period runoff process of each field of flood in the group to obtain a forecasting flow series S of each field of flood in the group;
s62, respectively calculating the empirical distribution of the field actual measurement flow series H and the corresponding forecast flow series S of each flood peak magnitude according to a mathematical expectation formula;
s63, selecting a marginal distribution series commonly used by hydrological variables, selecting marginal distributions best fitted with empirical distributions of the field flood actual measured flow series and the corresponding forecast flow series by utilizing a BIC (building information center) criterion as theoretical marginal distribution functions of the field flood actual measured flow series and the corresponding forecast flow series of the flood peak magnitude respectively, and recording the functions as gamma i And Λ i (ii) a The calculation formula of the BIC criterion is as follows:
BIC＝ln(n)k-2ln(L)
wherein, BIC is a BIC value of a certain marginal distribution; n is the number of samples, namely the total time period number of the field flood of the flood peak magnitude; k is the number of marginal distribution function parameters; l is a likelihood function;
s7, calculating a prior density function, a likelihood function and posterior distribution of the actually measured flow on the conversion space;
s8, a posterior distribution function of actual measurement flow of flood in the original space field is calculated;
and S9, forecasting the secondary flood runoff process by the hydrological uncertainty processor based on the quantitative change parameters.
2. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 1, characterized in that: the step S1 specifically includes the following contents,
s11, collecting a daily rainfall amount series and a rainfall amount extraction table of each rainfall station in a research flow field, a daily evaporation amount series of hydrological stations in the research flow field, a daily average flow amount series of an outlet section of the research flow field and a flood element extraction table;
s12, interpolating the runoff process of the field flood into a time-interval-by-time flow series with the time interval length of 1 hour by a linear interpolation method; interpolating the rainfall amount series in the field flood process into a time-interval-by-time rainfall amount series with the time interval length of 1 hour by a linear interpolation method; and calculating the surface average rainfall amount series and the surface average evaporation amount series of the research watershed by a Thiessen polygon method or an arithmetic mean method.
3. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 1, characterized in that: s2, specifically, constructing a hydrological model of daily scale suitable for researching runoff forecasting of the watershed, and calculating state variables of each day of the watershed by using the hydrological model to provide initial state variables for a secondary flood forecasting model of the watershed; the state variables comprise soil water content, runoff yield area ratio and free water storage capacity.
4. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 1, characterized in that: the step S3 specifically includes the following contents,
s31, constructing a secondary flood forecasting model suitable for researching the sink flow characteristics of the drainage basin, and setting constraint conditions;
s32, grouping and integrating the field floods of the research basin by taking the peak magnitude as a grouping condition, selecting representative groups of field floods and determining the starting and ending time of each field flood;
and S33, calculating initial state variables of flood of each field by using the hydrological model of daily scale constructed in the S2.
5. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 1, characterized in that: step S4, specifically, according to the optimization effect of the single-field flood, selecting a target function for each field flood; the target function is a residual sum of squares function or a peak weighted root mean square error function or a flood error percentage function or a comprehensive function comprehensively considering the influence of flood peak and flood.
6. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 1, characterized in that: step S7, specifically, converting a field flood actual measurement flow series H and a corresponding forecast flow series S with the same peak magnitude into a field flood actual measurement flow series W and a corresponding forecast flow series X in space through a normal quantile conversion method, and further calculating a prior density function, a likelihood function and posterior distribution of actual measurement flow in the conversion space; the formula for normal quantile conversion is as follows:
W i ＝Q -1 (Γ i (H i )),i＝1,2,…,12
X i ＝Q -1 (Γ i (S i )),i＝1,2,…,12
wherein, Q is a standard normal distribution function, and i is the ith time interval.
7. The method of real-time forecasting of flood hydrological models of a field based on quantitative parameter hydrological uncertainty processor of claim 6, characterized in that: step S8, specifically, the posterior distribution of the actual measurement flow series of the flood of the same field to be forecasted on a conversion space is converted into a posterior distribution function on an original space through a Jacobian formula; the Jacobian formula is:
J(y)＝m(y)/q(Q -1 (M(y)))
wherein m is a density function of a field flood actual measurement flow series H; y is a field flood actual measurement flow series of the same flood peak magnitude to be forecasted; q is a standard normal distribution density function; q is a standard normal distribution function; and M is an edge distribution function of the field flood actual measurement flow series H.
8. The method of real-time forecasting of flood hydrological models of a field based on a quantitative parameter hydrological uncertainty processor of claim 7, characterized in that: step S9 is specifically that the actual measurement flow series with the period length of 1 hour of the field flood with the same peak magnitude to be forecasted and the corresponding secondary flood forecasting model forecasting flow series are substituted into the posterior distribution of the actual measurement flow of the original space field flood obtained in step S8, the distribution function is randomly sampled through a random sampling method, quantiles of 50%, 2.5% and 97.5% of the actual measurement flow are calculated, the quantiles of 50% are used as the forecasting results of the real-time flow of the hydrological uncertainty processor, and the quantiles of 2.5% and 97.5% are used as the upper limit and the lower limit of the 95% confidence interval of the real-time flow forecasting.
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